Global–Local Feature Collaborative Fusion and Edge Refinement Network for Remote Sensing Change Detection

The remote sensing image change detection (RSI-CD) has witnessed significant progress. The core focus lies in accurately identifying spatiotemporal changes in remote sensing images and precisely extracting edge details. However, the current remote sensing image change detection (CD) primarily faces two challenges: the diversity and complexity of change regions and the blurred edges of change regions. These issues lead to pseudo-change interference and incomplete detection of change targets. To alleviate these limitations, this study first develops a local-global differential fusion module (LGDFM) that integrates local feature extraction with global self-attention mechanism to enhance change feature representation through the effective bitemporal image fusion. This module simultaneously captures both global and local information from source images, thereby overcoming the insufficient feature fusion in existing bitemporal CD methods. Subsequently, we propose an edge-aware enhancement module (EAEM) that employs morphological operations to extract edge features. The module progressively refines prediction results through multistage edge feature integration that ensures precise contour delineation. Experiments on two datasets demonstrate that the proposed method achieves competitive performance compared with existing approaches.